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Related Concept Videos

Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
FISH - Fluorescent In-situ Hybridization02:07

FISH - Fluorescent In-situ Hybridization

Fluorescence in situ hybridization, or FISH, was developed in the early 1980s and has quickly become one of the most widely used techniques in cytogenetics. Labeled probes are used to bind complementary DNA or RNA sequences on a chromosome or in a region within a cell. Earlier, the probes could only be obtained by cloning or reverse transcription of a DNA template. Currently, the probe oligonucleotides can be synthesized synthetically. Additionally, with the advancement of optical techniques,...
SNAREs and Membrane Fusion01:43

SNAREs and Membrane Fusion

Once a transport vesicle has recognized its target organelle, the vesicular membrane needs to fuse with the target membrane to unload the cargo. Transmembrane proteins called SNAREs present on organelle membranes and their vesicles, mediate vesicle fusion.
SNAREs exist in pairs that symmetrically interact and catalyze the fusion of the lipid bilayers in vesicle and target organelle. v-SNARE in the vesicle membrane are single polypeptide chains that bind to a complementary t-SNARE, composed of 2...
Nuclear Fusion02:45

Nuclear Fusion

The process of converting very light nuclei into heavier nuclei is also accompanied by the conversion of mass into large amounts of energy, a process called fusion. The principal source of energy in the sun is a net fusion reaction in which four hydrogen nuclei fuse and ultimately produce one helium nucleus and two positrons.
A helium nucleus has a mass that is 0.7% less than that of four hydrogen nuclei; this lost mass is converted into energy during the fusion. This reaction produces about...

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Related Experiment Video

Updated: May 23, 2026

Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing
09:49

Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing

Published on: July 5, 2019

Label fusion strategy selection.

Nicolas Robitaille1, Simon Duchesne

  • 1Centre de Recherche de l'Institut Universitaire en Santé Mentale de Québec, 2601, Chemin de la Canardière, QC, Canada G1J 2G3.

International Journal of Biomedical Imaging
|April 21, 2012
PubMed
Summary
This summary is machine-generated.

A new hybrid label fusion method, SVS, outperforms existing techniques like STAPLE, Voting, and Shape-Based Averaging (SBA) for medical image segmentation. SVS adaptively selects the best fusion strategy based on input data dissimilarity, improving accuracy.

Related Experiment Videos

Last Updated: May 23, 2026

Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing
09:49

Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing

Published on: July 5, 2019

Area of Science:

  • Medical image analysis
  • Computational anatomy
  • Image segmentation

Background:

  • Label fusion combines multiple segmentations for improved accuracy.
  • Existing methods (STAPLE, Voting, SBA) have limitations depending on data dissimilarity.

Purpose of the Study:

  • To develop and evaluate a novel hybrid label fusion technique (SVS).
  • To compare SVS against existing label fusion methods using simulated and real medical data.

Main Methods:

  • Developed SVS, an empirical hybrid technique that selects fusion methods based on input dissimilarity.
  • Evaluated STAPLE, Voting, SBA, and SVS on 2D and 3D simulated data.
  • Applied SVS to fuse 10 segmentations of hippocampus and amygdala from the ICBM dataset (78 subjects).

Main Results:

  • No single existing method (STAPLE, Voting, SBA) was universally superior.
  • SVS demonstrated superior performance compared to the three existing methods on simulated data.
  • On real data, SVS predominantly selected Shape-Based Averaging (SBA), indicating its effectiveness.

Conclusions:

  • SVS offers a more robust and accurate approach to label fusion in medical image segmentation.
  • The adaptive nature of SVS allows for optimal selection of fusion strategies, enhancing segmentation quality.